Effective customer engagement is a requisite for modern financial service providers that are adopting advanced methods to increase the level of personalization of their services [1]}. Although artificial intelligence (AI) has become a ubiquitous tool in financial technology [2]}, research in the field has yet to significantly advance levels of personalization [3]}. Asset management is an active research topic in AI for finance; however, the research opportunities presented by the need for personalized services are usually neglected [4]}. Whereas personalized investment advice is typically based on questionnaires, we propose the use of micro-segmentation based on spending behavior.
Traditionally, customer segmentation has been grounded in demographics which provide only a coarse segmentation [5]}; it fails to capture nuanced differences between individuals with the potential for undesirable ramifications, e.g. discrimination in credit scoring based on postal code [6]}. Micro-segmentation, however, provides a more sophisticated classification which can improve the quality of banking services [7]}, [8]}.
